Automated thumbnail selection for online video

ABSTRACT

Access is provided to optimal thumbnails that are extracted from a stream of video. Using a processing device configured with a model that incorporates preferences generated by the brain and behavior from the perception of visual images, the optimal thumbnail(s) for a given video is/are selected, stored and/or displayed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on, and claims priority to, U.S. ProvisionalApplication No. 61/796,555, filed Nov. 14, 2012, the entire contents ofwhich is fully incorporated herein by reference. This application isrelated to PCT Application No. PCT/US2013/028945, published as WO2013/131104, the entire contents of which is incorporated fully hereinby reference.

GOVERNMENT RIGHTS

This invention was made with government support under National ScienceFoundation NSFIIP1216835. The government has certain rights in thisinvention.

BACKGROUND OF THE INVENTION

For decades, psychologists regarded perception and affect as distinctprocesses. It was assumed that the perceptual system sees visualinformation and emotional networks evaluate affective properties. Theapplicant's research shows, however, that these processes are not soseparable, and that some affective components are in fact intimatelytied to perceptual processing (Lebrecht, S., Bar, M., Barrett, L. F. &Tarr, M. J. Micro-Valences: Perceiving Affective Valence in EverydayObjects. Frontiers in Psychology 3, (2012)). Applicant has shown thatvalence—the dimension of affect that represents positive to negative(Russell, J. A. A circumplex model of affect. Journal of personality andsocial psychology 39, 1161-1178 (1980))—is seen in the majority ofvisual information, and coded as part of the perceptual representation.Applicant has shown that valence perception is derived from acombination of low-level perceptual features and related associations,or highly similar features that results in an overall gist which thebrain then outputs as a single valence “score” that influences choicebehavior.

The second fundamental idea underlying this work is that valence doesnot need to be strong or obvious to exert an effect on behavior. Mostresearchers typically study strongly affective objects and scenes(Greenwald, A. G., McGhee, D. E. & Schwartz, J. L. Measuring individualdifferences in implicit cognition: the implicit association test. J PersSoc Psychol 74, 1464-1480 (1998); Avero, P. & Calvo, M. G. Affectivepriming with pictures of emotional scenes: the role of perceptualsimilarity and category relatedness. Span J Psychol 9, 10-18 (2006);Calvo, M. G. & Avero, P. Affective priming of emotional pictures inparafoveal vision: Left visual field advantage. Cognitive, Affective, &Behavioral Neuroscience 8, 41 (2008); Rudrauf, D., David, O., Lachaux,J. P., Kovach, C. K., et al. Rapid interactions between the ventralvisual stream and emotion-related structures rely on a two-pathwayarchitecture. J Neurosci 28, 2793-2803 (2008); Colibazzi, T., Posner,J., Wang, Z., Gorman, D., et al. Neural systems subserving valence andarousal during the experience of induced emotions. Emotion 10, 377-389(2010); Weierich, M. R., Wright, C. I., Negreira, A., Dickerson, B. C. &Barrett, L. F. Novelty as a dimension in the affective brain. Neuroimage49, 2871-2878 (2010)). While this is helpful for anchoring affectiveperception, it tells little about the typical objects encountered ineveryday life. Individual's perceive valence in almost all visualinformation that they encounter, and objects typically regarded as“neutral” by affective researchers in fact automatically generate theperception of a “micro”-valence. This work was confirmed by anintegrated mind and brain approach that included a series of perceptual,cognitive, and neuroimaging paradigms. Applicant was able tosuccessfully demonstrate that (a) one can measure an individual'sperception of micro-valence, (b) it relates to choice, (c) it is codedby the same neural mechanisms that code for strongly affective objects,and (d) the valence is processed by regions that code exclusively forobjects (Lebrecht, S. & Tarr, M. Defining an object's micro-valencethrough implicit measures. Journal of Vision 10, 966 (2010); Lebrecht,S., Bar, M., Sheinberg, D. L. & Tarr, M. J. Micro-Valence: Nominallyneutral visual objects have affective valence. Journal of Vision 11,856-856 (2011); Lebrecht, S., Johnson, D. & Tarr, M. J. [in revision]The Affective Lexical Priming Score. Psychological Methods).

Through behavioral experiments, Applicant has found that there is astrong consensus in valence perception across a constrained demographic.This remarkable consensus in the perception of objects previouslyregarded as “neutral” offers significant potential for the field ofconsumer behavior. The evidence that valence perception operates on acontinuum that can be quantified was uncovered during a subsequent fMRIexperiment. Of particular interest, Applicant found that the perceptionof micro-valence is coded by the same neural system that codes forstrong valence. This suggests that valence strength may be organizedtopologically. The Region of Interest (ROI) analysis has also shown howthe perception of valence varies as a function of percent signal change.

In recent years, the online video landscape has evolved significantlyfrom primarily featuring user-generated content to delivering morepremium-content videos such as TV episodes, news clips, and full-lengthmovies identical to what a user would otherwise watch on TV. Growth inthe amount of professionally-produced content available online has ledto a parallel increase in video length, creating more opportunity forpre-roll and in-stream video ads; Advertisers have already started totake advantage. While YouTube® continues to dominate the online videomarket in terms of total videos viewed each month, for twenty-fourconsecutive months since June 2010, Hulu®, the leading platform forpremium content, generated the highest number of video ad views everymonth according to comScore® (“ComScore Launches Video Metrix 2.0 toMeasure Evolving Web Video Landscape.” ComScore announces improvementsto video measurement service and releases Video Metrix rankings for June2010. Jul. 15, 2010. comScore. Web. 15 Jun. 2012,http://www.comscore.com/Press_Events/Press_Releases/2010/7/comScore_Launches_Video_Metrix_(—)2.0_to_Measure_Evolving_Web_Video_Landscape).Since the number of long-form videos online is expected to continue togrow substantially in coming years, a similar increase in the number ofin-stream video ads is likely.

While a massive market opportunity lies in the digital advertisingspace, the opportunity coming from the use of digital video in the webcommerce industry should not be overlooked. Digital video is now beingused for product demonstrations at the point of purchase, for example.As online spending and competition grows, these types of videos arealready providing a competitive edge—internet retailers that offerproduct videos have seen increased sales and decreased returns forproducts with video descriptions. In 2009, the online shoe-sellingpowerhouse Zappos.com® reported increased sales ranging from 6-30% forproducts that had a video description (Changing the Way You Shop forShoes. Interview with Zappos.com's Senior Manager Rico Nasol on how theretailer is using streaming video to boost sales. Video. Streaming MediaWest: FOXBusiness.com, Dec. 4, 2009.http://video.foxbusiness.com/v/3951649/changing-the-wayyou-shop-for-shoes/).

BRIEF SUMMARY OF THE INVENTION

In one aspect of the present disclosure, a method performed by one ormore processing devices includes retrieving data for a video frame froma video stream that has the highest positive Affective Valence, and assuch serves as the most effective representative thumbnail.

Embodiments of the disclosure can include one or more of the followingimplementations. Affective Valence, a signal generated during visualperception that informs choice and decision-making structures in thehuman brain, can be assessed experimentally using behavioral methods.Affective Valance can also be assessed using functional magneticresonance imaging (fMRI).

In another aspect of the present disclosure, understanding that videoframes do not have to generate a strong or arousing affective perceptionin order to be coded by the neural system that does represent stronglyaffective information is a fundamental insight for areas of industrythat require images to communicate information and elicit behavior.Moreover, the ability to read out the relative valence perceptionsdirectly from this neural continuum provides a valuable tool that needsto be translated into a product that online video publishers andadvertisers could benefit from.

In another aspect of the present disclosure, the experimental methods ofthe mental and neural codes for the valence of images are translatedinto a tractable model capable of generating reliable predictions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. The right-hand side of the diagram illustrates how thethumbnails are extracted from a stream of video and run through acomputational model, which outputs a recommended thumbnail. The leftthree boxes represent the components of the computational model;

FIG. 2 is a diagram of an example of a computer system on which one ormore of the functions of the embodiment may be implemented.

DETAILED DESCRIPTION OF THE INVENTION

In one embodiment, a crowd-compute model is used to predict the mostvisually appealing thumbnail from a stream of video (FIG. 1). Thissystem is a computationally intensive model that integrates AffectiveValence perceptions, big data, computer vision, and machine learning.

In one example, the thumbnail extractor model is a weighted averagecrowd-compute model with three weights: [1] the perception of valence(behavior), [2] the neural representation of valence (brain), and [3]the crowd-sourced perception of valence. To generate the behavioralweight, in one embodiment, a database is used containing a large numberof thumbnails along with their perceived valence scores. To estimate thevalence of a thumbnail, image similarity metrics are used to match thenovel thumbnail to the most similar thumbnails in the database. Togenerate the brain weight, a similar technique is used, except thedatabase of thumbnails is tied to their associated neural responses asestimates of perceived valence.

In one example, participants are 18-60 years old with normal orcorrected-to-normal vision. MRI participants are right handed andscreened for neurological and psychiatric disorders in addition tomethod-specific contraindications for participation in MRI.

In one example, behavioral data is collected for thumbnails from videos,where thumbnails are defined as colored photographic screenshots, in oneexample, that range in size. Thumbnails represent, in one example, thefollowing categories: news, sports, TV, music, education, user-generatedcontent, screencasts, demonstration videos, marketing, and advertising.In one example, MR data is collected on a number of representativethumbnails from each of the different categories listed above.

In one example, behavioral data is collected via online crowd-sourcingplatforms, in one example, Mechanical Turk, where large amounts of humanresponse data can be acquired rapidly from a variety of differentdemographics.

In one example, the perceived valence of the thumbnails is measure usinga version of the “Birthday Task,” which has been used previously topredict the valence of everyday objects and their underlying neuralrepresentation. On any given trial, participants are presented withthree thumbnails from the same video and asked to click the video theywould most like to watch. This part of the experiment is repeated asecond time, except participants are asked to click the video they wouldleast like to watch (order counter-balanced across participants). Eachtriplet is presented for, in one example, less than 1000 ms multipliedby the number of images that appear in the triplet, and video frames arerepeated in unique triplets, in each condition to establish responseconsistency. The most and least conditions are designed to indexpositive and negative dimensions of valence, respectively.

In one example, data is analyzed using a statistical software package.To calculate a valence score for each thumbnail, the difference is takenbetween the number of times a particular frame is selected in the mostcondition from the number of times it is selected in the leastcondition. The valence of each thumbnail for each participant iscalculated, in addition to averaging individual participants' scores togenerate a single average group score for each thumbnail. In oneembodiment, the model is able to dynamically adjust the group averagescore for each thumbnail based on set parameters. For example, the groupscore can be calculated from all participants' data, or only a subsetbased on specified age ranges or other demographics. This allows themodel to predict the best thumbnail for different user demographicgroups.

In one example, the fMRI experiment is used to generate a score thatcontains a valence and strength value for each thumbnail based on theirunderlying neural response. In one example, a computer programminglanguage is used to conduct the experiment. Thumbnails are presented on,in one example, an MR-compatible high-resolution 24-inch LCD visualdisplay (e.g., Cambridge Research BOLDScreen) that participants viewthrough a mirror attached to the head coil. Participants see a thumbnailcentered on a black screen for a period of time less than 1500 ms, andare asked to rate the thumbnail for pleasantness on a continuous numberscale that can vary from 1-10. This attention task has been in aprevious fMRI experiment that successfully located the cortical regionthat represents the continuous perception of valence. Button responsesare recorded using an MR-compatible response glove or button box.Participants are able to respond while the thumbnail is on the screen orduring a response window. Experimental trials will be structured tomaximize signal and minimize noise based on standard functional MRIpractices. After the fMRI part of the experiment, each participant willcomplete a series of demographic questions.

In one example, whole brain imaging is performed using, in one example,a Siemens 3T Verio MR scanner equipped with 32-channel phase-array headcoil. Head motion is minimized using the MR center's head restraintsystem. A high-resolution T1-weighted 3D MPRAGE anatomical image istaken (e.g., 1 mm isotropic voxels; 40 slices) followed by functionalimages collected using a gradient echo, echo-planar sequence (e.g.,TR=1900 ms, TE=2.98 ms). Prior to preprocessing, in-depth data qualitychecks are performed on every subject to identify the presence ofexcessive head motion or rare signal artifacts. Participants that movemore than 3 mm are excluded from analysis. EPI images are corrected forslice time acquisition, motion, normalized to standard space(Talairach), and spatially smoothed with an 8 mm FWHM isotropic Gaussiankernel.

In one example, functional data is analyzed using, for example, SPM8 toconstruct a within-subject statistical model under the assumptions ofthe general linear model. To compare activation across experimentalconditions, effects are estimated using a subject-specific fixed effectsmodel with session effects treated as confounds. To compare individualsubject effects, the estimates are entered into a second-level groupanalysis where subject becomes a random effect. The statistical test isa one-sample t-test against a contrast value of zero for each voxel. Thewhole brain contrasts are supported by region of interest (ROI) analysesthat show quantitative changes in signal in a specified region.

In one example, a Region of Interest (ROI) analysis is conducted using,for example, the SPM8 ROI MARSBAR Toolbox. ROIs are defined anatomicallybased on co-ordinates from supporting work, and functionally usingunbiased contrasts. A region that is centered in the right InferiorFrontal Sulcus is selected using the MNI co-ordinates from our previousstudy where we located valence processing and extracted evidence for thevalence continuum. ROIs include voxels within, for example, an 8 mmradius extending from the center of the defined region. Selectiveaveraging will permit extraction of peak percent signal changesassociated with each condition. In this analysis, each thumbnail istreated as a condition by averaging across the thumbnail repetitions. Inaddition, the integrated percent signal change is extracted for eachthumbnail. ROI data is visualized using, for example, MATLAB and Prism.Whole brain data is visualized using a combination of, for example,MRICRON and the SPM Surfrend Toolbox.

In one example, thumbnails with a stronger BOLD response in the InferiorFrontal Sulcus and surrounding regions in the prefrontal cortex have amore positive perceive valence, meaning that users are more likely toclick on them.

In one embodiment, a stream of novel thumbnails (e.g., a video) aremapped into the behavioral and brain thumbnail spaces established usingthe above methods. In one example, Scene Gist14 (Leeds, D. D., D. A.Seibert, J. A. Pyles, and M. J. Tarr. “Unraveling the Visual andSemantic Components of Object Representation.” 11th Annual Meeting ofthe Vision Sciences Society. Poster. May 6, 2011. Address) is used tomatch a novel thumbnail probe to reference thumbnails with knownvalences in the brain and behavior databases. Scene Gist works byrepresenting each image as a weighted set of components (derived fromPrinciple Component Analysis), where each component captures a commonspatial frequency property of natural scenes. Features are consideredcomponent weights. Unlike many other image representational approachesin computer vision, Scene Gist incorporates color (which may be acritical component in the perception of valence). Overall, it isdesigned to model the early stages of visual processing that are activewhen you first encode the gist of a scene, rather than individualobjects, which is critical for matching scenes in thumbnail images.

In one embodiment, the thumbnail extractor model works by using SceneGist to match the probe thumbnail to the set of closest referencethumbnails in both the brain and the behavioral databases. With respectto the brain database, once Scene Gist has identified the closestreference thumbnail, the probe assigns a valence score based on those ofthe reference thumbnails. This score provides the brain weight in themodel. There is, however, the potential that Scene Gist maps the probethumbnail to reference thumbnails of very different valences. Therefore,the weight given to each database within the model considers thevariance within the reference set in image matching success. This meansthat if the probe thumbnail maps to various reference thumbnails withconflicting valences, the variance is high and the overall weight on thebrain database for that particular probe thumbnail would be low, therebycontrolling for potentially erroneous predictions.

In one example, in order to validate predictions from the brain andbehavioral databases, a valence perception for the probe thumbnail iscrowd sourced. This constitutes the third weight in the crowd-computemodel. The crowd sourcing is a shortened version of the Birthday Taskdescribed earlier. This allows for rapid validation of thumbnailprediction across a number of participants.

Weights for the brain and behavioral database vary as a function of thevariance in the performance of Scene Gist. In one example, weights foreach component are trialed and tested by setting initial weights,monitoring real-world click rates, and adjusting them based on the clickdata. Once the weights have been set, the overall prediction score for agiven thumbnail is comprised of the weighted average of the three modelcomponents. This weighted score changes based on the demographicinformation that is parsed through the model based on the customers'requests.

In one embodiment, the system can deliver thumbnails specific todifferent user groups. In one example, demographic variables thatcustomers use are used to define their user groups so that the same datacan be collected from participants contributing to the databases. Withthis information, the model can be selectively restricted to search fora thumbnail based on the specific demographics. In one example, thesystem selects a sport thumbnail for Caucasian males between the ages of40 and 60 that often watch cricket. To achieve dynamic, user-specificpredictions, a battery of questions is formulated that include questionson age, gender, race, ethnicity, education level, income, interests,hobbies, most-visited websites, social network participation, daily TVusage, daily online usage, and estimated online video viewing frequency.These demographic questions are augmented with customer-suppliedquestions that together are used to define users so that our product canmost effectively generate targeted thumbnail selections.

In one embodiment, computer vision methods are used to extract morefine-grained descriptors of each frame, including but not limited tosemantics, color composition, complexity, people, and animals (thenumber of descriptors is limited to the robustness of the differentmethods available).

In one embodiment, using frames that have been tagged for valencethrough crowd-sourcing, several computational tools are then used toexplore which of these descriptors explain the greatest amount ofvariance in valence. In one example, split-half, test-generalize methodsare used to establish the efficacy of this piecewise approach topredicting valence.

The system can be used in various applications. Editorial video, forexample, includes TV shows, movies, webisodes, trailers, and clips frommajor commercial broadcasting networks such as NBC, Fox, ABC, ESPN, andCNN. The utility of the present invention for owners of editorial videosis in part that increased click rates means increased time spent on thesite, user engagement, and advertising revenue.

In one example, the system can be used in video marketing andadvertising. The popularity of online video reflects its potential toserve as a mass market medium, and as a new tool for brands to reachconsumers. Videos in the marketing segment range from traditional videoads placed as content, to product demonstrations and tutorial videos.Each of these types of marketing videos have been shown to increaseconversion rates, brand loyalty, and for internet retailers, sales andbasket sizes.

In one example, the system can be used as an educational video. This isa growing segment of the online video industry.

A system for performing the described functions can comprise a generalpurpose computer configured in a known manner to perform the functions.Entities for performing the functions described can reside in a singleprocessor so configured or in separate processors, e.g., an analysisprocessor, in identification processor, a designation processor, acalculation processor, a video display processor, a video frame analysisprocessor, a valence data processor, and the like. These entities may beimplemented by computer systems such as computer system 1000 as shown inFIG. 2, shown by way of example. Embodiments of the present inventionmay be implemented as programmable code for execution by such computersystems 1000. After reading this description, it will become apparent toa person skilled in the art how to implement the invention using othercomputer systems and/or computer architectures, including mobile systemsand architectures, and the like.

Computer system 1000 includes one or more processors, such as processor1004. Processor 1004 may be any type of processor, including but notlimited to a special purpose or a general-purpose digital signalprocessor. Processor 1004 is connected to a communication infrastructure1006 (for example, a bus or network).

Computer system 1000 also includes a user input interface 1003 connectedto one or more input device(s) 1005 and a display interface 1007connected to one or more display(s) 1009. Input devices 1005 mayinclude, for example, a pointing device such as a mouse or touchpad, akeyboard, a touch screen such as a resistive or capacitive touch screen,etc.

Computer system 1000 also includes a main memory 1008, preferably randomaccess memory (RAM), and may also include a secondary memory 610.Secondary memory 1010 may include, for example, a hard disk drive 1012and/or a removable storage drive 1014, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, etc. Removable storagedrive 1014 reads from and/or writes to a removable storage unit 1018 ina well-known manner. Removable storage unit 1018 represents a floppydisk, magnetic tape, optical disk, etc., which is read by and written toby removable storage drive 1014. As will be appreciated, removablestorage unit 1018 includes a computer usable storage medium havingstored therein computer software and/or data.

In alternative implementations, secondary memory 1010 may include othersimilar means for allowing computer programs or other instructions to beloaded into computer system 1000. Such means may include, for example, aremovable storage unit 1022 and an interface 1020. Examples of suchmeans may include a program cartridge and cartridge interface (such asthat previously found in video game devices), a removable memory chip(such as an EPROM, or PROM, or flash memory) and associated socket, andother removable storage units 1022 and interfaces 1020 which allowsoftware and data to be transferred from removable storage unit 1022 tocomputer system 1000. Alternatively, the program may be executed and/orthe data accessed from the removable storage unit 1022, using theprocessor 1004 of the computer system 1000.

Computer system 1000 may also include a communication interface 1024.Communication interface 1024 allows software and data to be transferredbetween computer system 1000 and external devices. Examples ofcommunication interface 1024 may include a modem, a network interface(such as an Ethernet card), a communication port, a Personal ComputerMemory Card International Association (PCMCIA) slot and card, etc.Software and data transferred via communication interface 1024 are inthe form of signals 1028, which may be electronic, electromagnetic,optical, or other signals capable of being received by communicationinterface 1024. These signals 1028 are provided to communicationinterface 1024 via a communication path 1026. Communication path 1026carries signals 1028 and may be implemented using wire or cable, fiberoptics, a phone line, a wireless link, a cellular phone link, a radiofrequency link, or any other suitable communication channel. Forinstance, communication path 1026 may be implemented using a combinationof channels.

The terms “computer program medium” and “computer usable medium” areused generally to refer to media such as removable storage drive 1014, ahard disk installed in hard disk drive 1012, and signals 1028. Thesecomputer program products are means for providing software to computersystem 1000. However, these terms may also include signals (such aselectrical, optical or electromagnetic signals) that embody the computerprogram disclosed herein.

Computer programs (also called computer control logic) are stored inmain memory 1008 and/or secondary memory 1010. Computer programs mayalso be received via communication interface 1024. Such computerprograms, when executed, enable computer system 1000 to implementembodiments of the present invention as discussed herein. Accordingly,such computer programs represent controllers of computer system 1000.Where the embodiment is implemented using software, the software may bestored in a computer program product 1030 and loaded into computersystem 1000 using removable storage drive 1014, hard disk drive 1012, orcommunication interface 1024, to provide some examples.

Alternative embodiments may be implemented as control logic in hardware,firmware, or software or any combination thereof.

It will be understood that embodiments of the present invention aredescribed herein by way of example only, and that various changes andmodifications may be made without departing from the scope of theinvention.

In the embodiment described above, the mobile device stores a pluralityof application modules (also referred to as computer programs orsoftware) in memory, which when executed, enable the mobile device toimplement embodiments of the present invention as discussed herein. Asthose skilled in the art will appreciate, the software may be stored ina computer program product and loaded into the mobile device using anyknown instrument, such as removable storage disk or drive, hard diskdrive, or communication interface, to provide some examples.

As a further alternative, those skilled in the art will appreciate thatthe hierarchical processing of words or representations themselves, asis known in the art, can be included in the query resolution process inorder to further increase computational efficiency.

These program instructions may be provided to a processor to produce amachine, such that the instructions that execute on the processor createmeans for implementing the functions specified in the illustrations. Thecomputer program instructions may be executed by a processor to cause aseries of operational steps to be performed by the processor to producea computer-implemented process such that the instructions that executeon the processor provide steps for implementing the functions specifiedin the illustrations. Accordingly, the figures support combinations ofmeans for performing the specified functions, combinations of steps forperforming the specified functions, and program instruction means forperforming the specified functions.

The claimed system can be embodied using a processing system, such as acomputer, having a processor and a display, input devices, such as akeyboard, mouse, microphone, or camera, and output devices, such asspeakers, hard drives, and the like. This system comprises means forcarrying out the functions disclosed in the claims (Means for exposing,means for calculating, means for storing, means for providing, means forcorrelating, etc.).

While there has been described herein the principles of the invention,it is to be understood by those skilled in the art that this descriptionis made only by way of example and not as a limitation to the scope ofthe invention. Accordingly, it is intended by the appended claims, tocover all modifications of the invention which fall within the truespirit and scope of the invention. Further, although the presentinvention has been described with respect to specific preferredembodiments thereof, various changes and modifications may be suggestedto one skilled in the art and it is intended that the present inventionencompass such changes and modifications as fall within the scope of theappended claims.

REFERENCES

-   1. Lebrecht, S., Bar, M., Barrett, L. F. & Tarr, M. J.    Micro-Valences: Perceiving Affective Valence in Everyday Objects.    Frontiers in Psychology 3, (2012).-   2. Russell, J. A. A circumplex model of affect. Journal of    personality and social psychology 39, 1161-1178 (1980).-   3. Greenwald, A. G., McGhee, D. E. & Schwartz, J. L. Measuring    individual differences in implicit cognition: the implicit    association test. J Pers Soc Psychol 74, 1464-1480 (1998).-   4. Avero, P. & Calvo, M. G. Affective priming with pictures of    emotional scenes: the role of perceptual similarity and category    relatedness. Span J Psychol 9, 10-18 (2006).-   5. Calvo, M. G. & Avero, P. Affective priming of emotional pictures    in parafoveal vision: Left visual field advantage. Cognitive,    Affective, & Behavioral Neuroscience 8, 41 (2008).-   6. Rudrauf, D., David, O., Lachaux, J. P., Kovach, C. K., et al.    Rapid interactions between the ventral visual stream and    emotion-related structures rely on a two-pathway architecture. J    Neurosci 28, 2793-2803 (2008).-   7. Colibazzi, T., Posner, J., Wang, Z., Gorman, D., et al. Neural    systems subserving valence and arousal during the experience of    induced emotions. Emotion 10, 377-389 (2010).-   8. Weierich, M. R., Wright, C. I., Negreira, A., Dickerson, B. C. &    Barrett, L. F. Novelty as a dimension in the affective brain.    Neuroimage 49, 2871-2878 (2010).-   9. Lebrecht, S. & Tan, M. Defining an object's micro-valence through    implicit measures. Journal of Vision 10, 966 (2010).-   10. Lebrecht, S., Bar, M., Sheinberg, D. L. & Tan, M. J.    Micro-Valence: Nominally neutral visual objects have affective    valence. Journal of Vision 11, 856-856 (2011).-   11. Lebrecht, S., Johnson, D. & Tan, M. J. [in revision] The    Affective Lexical Priming Score. Psychological Methods.-   12. Lang, P. J., Bradley, M. M. & Cuthbert, B. N. International    affective picture system (IAPS): Technical manual and affective    ratings. NIMH Center for the Study of Emotion and Attention (1997).-   13. Leeds, D. D., D. A. Seibert, J. A. Pyles, and M. J. Tarr.    “Unraveling the Visual and Semantic Components of Object    Representation.” 11th Annual Meeting of the Vision Sciences Society.    Poster. May 6, 2011. Address.-   14. Oliva, A. & Torralba, A. Modeling the shape of the scene: A    holistic representation of the spatial envelope. International    Journal of Computer Vision 42, 145-175 (2001).-   15. Verna, Paul. “Top Digital Trends for 2012.” Top Digital Trends    for 2012. December, 2011.    http://www.theaimagency.com/wp-content/uploads/2012/03/15111_eMarketer_Top_Digital_Trends_(—)2012.pdf.-   16. “ComScore Releases April 2012 U.S. Online Video Rankings.” May    18, 2012. comScore. Web. 15 Jun. 2012.    <http://www.comscore.com/Press_Events/Press_Releases/2012/5/comScore_Releases_April_(—)2012_U.S._Online_Video_Rankings.-   17. IAB Internet Advertising Revenue Report. 2007 Full Year Results.    PricewaterhouseCoopers, May 23, 2008.    http://www.iab.net/media/file/IAB_PwC_(—)2007_full_year.pdf.-   18. IAB Internet Advertising Report. 2011 Full Year Results.    PricewaterhouseCoopers, Apr. 18, 2012.    <http://www.iab.net/media/file/IAB    Internet_Advertising_Revenue_Report_FY_(—)2011.pdf.-   19. VanBoskirk, Shar. US Interactive Marketing Forecast, 2011    to 2016. Forrester Research, Aug. 24, 2011.-   20. “ComScore Launches Video Metrix 2.0 to Measure Evolving Web    Video Landscape.” ComScore announces improvements to video    measurement service and releases Video Metrix rankings for    June 2010. Jul. 15, 2010. comScore. Web. 15 Jun. 2012.    http://www.comscore.com/Press_Events/Press_Releases/2010/7/comScore_Launches_Video_Metrix_(—)2.0_to_Measure_Evolving_Web_Video_Landscape.-   21. Explosive Online Video Growth Coming From Greater Engagement    Around Premium Programming. Interview with Eli Goodman, comScore    Media Evangelist, from Beet.TV's 2012 Executive Retreat. Video.    Beet.TV Executive Retreat, Vieques, Puerto Rico: Beet.TV, Mar.    29, 2012. Print.    http://www.beet.tv/2012/03/comscore-eli-goodman.html.-   22. Digital Video Advertising Trends: 2012. An annual study    examining growth in digital video advertising. Break Media, 2011.-   23. Changing the Way You Shop for Shoes. Interview with Zappos.com's    Senior Manager Rico Nasol on how the retailer is using streaming    video to boost sales. Video. Streaming Media West: FOXBusiness.com,    Dec. 4, 2009.    http://video.foxbusiness.com/v/3951649/changing-the-wayyou-shop-for-shoes/.

We claim:
 1. An automated method for determining an optimal video framefrom a video stream comprising a plurality of video frames, the methodcomprising: Analyzing, via a processing device, each of said videoframes to obtain data indicative of a desired property for each videoframe; identifying one or more video frames in the video stream having alevel of said desired property above a predetermined threshold level;and designating the one or more identified video frames as the optimalvideo frames.
 2. The method of claim 1, wherein said data indicative ofa desired property comprises valence data.
 3. The method of claim 2,wherein said identified video frames comprise the video frames having apositive Affective Valence above a predetermined threshold level.
 4. Themethod of claim 3, wherein said the Affective Valence is determined by:exposing at least one individual, via a processing device, to at leastone valence-measuring paradigm in which the at least one individual isexposed to a said plurality of video frames and is required to provide aresponse directed to at least one of said video frames; calculating avalence value for each of said plurality of video frames based on eachresponse; and storing each valence value in a storage medium, whereinsaid response and a speed within which said response was given enablesan inference to be made regarding an implicit attitude of the individualtowards said at least one of said plurality of video frames.
 5. Themethod of claim 4, wherein the stored valence values are used to predicthow individuals will react to being exposed to video frames to whichthey may not have been previously exposed.
 6. The method of claim 4,wherein said at least one individual is exposed, via a processingdevice, to multiple valence-measuring paradigms, in each of which the atleast one individual is exposed to a plurality of video frames andprovides a response directed to at least one of said plurality of videoframes; calculating a valence value for each of said plurality of videoframes based on each response; and storing each valence value in astorage medium.
 7. The method of claim 6, comprising a firstvalence-measuring paradigm that includes a behavioral valence measuringtechnique and a second valence measuring paradigm that includes aneuroimaging valence measuring technique.
 8. The method of claim 7,wherein valence values for a particular one of said video frames foreach of said paradigms are correlated, thereby providing a basis forassessing a confidence level of the valence values for said particularone of said video frames.
 9. The method of claim 8, wherein thecorrelated valence values are used to give a distributed representationof valence.
 10. The method of claim 4, wherein said at least onevalence-measuring paradigm comprises a behavioral valence measuringtechnique.
 11. The method of claim 4, wherein said at least onevalence-measuring paradigm comprises a neuroimaging valence measuringtechnique.
 12. The method of claim 4, wherein said at least onevalence-measuring paradigm measures a positive dimension of valence. 13.The method of claim 4, wherein said at least one valence-measuringparadigm measures a negative dimension of valence.
 14. An automatedsystem for determining an optimal video frame from a video streamcomprising a plurality of video frames, comprising a processorconfigured to: analyze each of said video frames to obtain dataindicative of a desired property for each video frame; identify one ormore video frames in the video stream having a level of said desiredproperty above a predetermined threshold level; and designate the one ormore identified video frames as the optimal video frames.
 15. The systemof claim 14, wherein said data indicative of a desired propertycomprises valence data.
 16. The system of claim 15, wherein saididentified video frames comprise the video frames having a positiveAffective Valence above a predetermined threshold level.
 17. The systemof claim 16, wherein said the Affective Valence is determined by aprocessing device configured to: expose at least one individual to atleast one valence-measuring paradigm in which the at least oneindividual is exposed to a said plurality of video frames and isrequired to provide a response directed to at least one of said videoframes; calculate a valence value for each of said plurality of videoframes based on each response; and store each valence value in a storagemedium, wherein said response and a speed within which said response wasgiven enables an inference to be made regarding an implicit attitude ofthe individual towards said at least one of said plurality of videoframes.
 18. The system of claim 17, wherein the stored valence valuesare used to predict how individuals will react to being exposed to videoframes to which they may not have been previously exposed.
 19. Thesystem of claim 17, wherein said at least one individual is exposed, viaa processing device, to multiple valence-measuring paradigms, in each ofwhich the at least one individual is exposed to a plurality of videoframes and provides a response directed to at least one of saidplurality of video frames, said processing device further configured to;calculate a valence value for each of said plurality of video framesbased on each response; and store each valence value in a storagemedium.
 20. The system of claim 19, wherein a first valence-measuringparadigm includes a behavioral valence measuring technique and a secondvalence measuring paradigm includes a neuroimaging valence measuringtechnique.
 21. The system of claim 20, wherein valence values for aparticular one of said video frames for each of said paradigms arecorrelated, thereby providing a basis for assessing a confidence levelof the valence values for said particular one of said video frames. 22.The system of claim 21, wherein the correlated valence values are usedto give a distributed representation of valence.
 23. The system of claim17, wherein said at least one valence-measuring paradigm comprises abehavioral valence measuring technique.
 24. The system of claim 17,wherein said at least one valence-measuring paradigm comprises aneuroimaging valence measuring technique.
 25. The system of claim 17,wherein said at least one valence-measuring paradigm measures a positivedimension of valence.
 26. The system of claim 17, wherein said at leastone valence-measuring paradigm measures a negative dimension of valence.